Unbiased proteomic profiling of host cell extracellular vesicle composition and dynamics upon HIV‐1 infection

Abstract

Cells release diverse types of extracellular vesicles (EVs), which transfer complex signals to surrounding cells. Specific markers to distinguish different EVs (e.g. exosomes, ectosomes, enveloped viruses like HIV) are still lacking. We have developed a proteomic profiling approach for characterizing EV subtype composition and applied it to human Jurkat T cells. We generated an interactive database to define groups of proteins with similar profiles, suggesting release in similar EVs. Biochemical validation confirmed the presence of preferred partners of commonly used exosome markers in EVs: CD81/ADAM10/ITGB1, and CD63/syntenin. We then compared EVs from control and HIV‐1‐infected cells. HIV infection altered EV profiles of several cellular proteins, including MOV10 and SPN, which became incorporated into HIV virions, and SERINC3, which was re‐routed to non‐viral EVs in a Nef‐dependent manner. Furthermore, we found that SERINC3 controls the surface composition of EVs. Our workflow provides an unbiased approach for identifying candidate markers and potential regulators of EV subtypes. It can be widely applied to in vitro experimental systems for investigating physiological or pathological modifications of EV release.

Development of Extracellular Vesicle Therapeutics: Challenges, Considerations, and Opportunities

“…A solution to this is functional assessment of each batch [cell source consistency ( Viaud et al, 2011 ; Andriolo et al, 2018 ; Mendt et al, 2018 ) and EV production for specific patient-derived cell source based on EV yield ( Mendt et al, 2018 ), positive/negative EV markers ( Andriolo et al, 2018 ; Mendt et al, 2018 ), reviewed in Nguyen et al (2020) ]; identification and quantification of key markers indicating functional competency could aid this process ( van Balkom et al, 2019 ) [i.e., tissue-based immunoregulation ( Ma et al, 2020 )]. Mapping specifically regulated proteins using microfluidic approaches ( Zhao et al, 2016 ; Fang et al, 2017 ; Xu et al, 2018 ) or quantitative proteomics onto known protein networks has highlighted mechanism of action ( Martin-Jaular et al, 2021 ) and is being applied to EV research (manufacture, composition, and function). Proteomic approaches allow researchers to understand protein signatures of native and engineered EVs ( Nasiri Kenari et al, 2019 ; van Balkom et al, 2019 ), which may have implications in quality control platforms to confirm the identity and test for purity of therapeutic EVs.…”

“…The International Society for EVs (ISEV) recommends the use of “EV” as a broad classifier term for these types of vesicles, due to the difficulty in assigning an EV to a particular biogenesis pathway, and instead recommends classifying EVs by their physical attributes (size, density), their differing biochemical composition, and surface charge ( Thery et al, 2018 ). The nature and relative abundance of EV cargo is selectively determined during EV biogenesis ( Palmulli and van Niel, 2018 ; van Niel et al, 2018 ; Clancy et al, 2021 ), and varies according to EV subtype and state/type of the producing cell ( Kowal et al, 2016 ; Xu et al, 2016 ; Zabeo et al, 2017 ; Greening and Simpson, 2018 ; Martin-Jaular et al, 2021 ). Importantly, EVs, comprising of a lipid membrane and aqueous lumen ( Cvjetkovic et al, 2016 ; Skotland et al, 2019 ), provide a pathway for the transfer of hydrophobic and hydrophilic components allowing for complex intercellular signaling ( Luga et al, 2012 ; Cossetti et al, 2014 ; de Couto et al, 2017 ; Kamerkar et al, 2017 ; Nabet et al, 2017 ; Wang and Lu, 2017 ; Flaherty et al, 2019 ; Han et al, 2019 ).…”

“…Findings from such studies have inspired strategies for EV-based therapeutic development, such as the modification of their contents to perform a specified function for a specific disease phenotype ( Table 3 ) ( Al-Nedawi et al, 2008 ; Hall et al, 2016 ; Yim et al, 2016 ; Greening et al, 2017 ; Yuan et al, 2017 ; Zhang G. et al, 2017 ; Chen R. et al, 2019 ; Shi et al, 2019 ; Skotland et al, 2019 ; O’Brien et al, 2020 ; Roefs et al, 2020 ). Comprehensive deciphering of EV biochemical and biophysical heterogeneity ( Jeppesen et al, 2019 ), variable composition ( Chen et al, 2016 ; Kowal et al, 2016 ; Greening et al, 2017 ; Jeppesen et al, 2019 ; Martin-Jaular et al, 2021 ), pharmacokinetic behavior ( Gupta et al, 2020 ), and functional diversity needs to be addressed in order to harness their potential as next generation therapeutics.…”

Understanding extracellular vesicle and nanoparticle heterogeneity: Novel methods and considerations

“…Currently proteomic approaches, most commonly label free, are used to characterize the composition of EV isolates and different EV populations [ 109 ]. Metabolic labelling based quantification such as stable isotope labelling of amino acids in cell culture (SILAC) can provide extremely accurate data about EV composition in cell culture models and is capable of being extended to mouse models, providing a powerful tool for resolution of EV s associated proteins [ 110 , 111 ].…”

Roles of Virion-Incorporated CD162 (PSGL-1), CD43, and CD44 in HIV-1 Infection of T Cells

“…Gag colocalizes with a subset of uropod-directed transmembrane proteins, namely, CD162, also known as a P-selectin glycoprotein ligand-1 (PSGL-1); CD43; and CD44, in polarized T cells, and even in non-polarized cells these proteins cocluster with Gag multimers on the cell surface and are incorporated into virus particles. Therefore, it is likely that these uropod proteins are incorporated into progeny Viruses 2021, 13,1935 2 of 12 virions specifically rather than passively [5,[18][19][20][21][22][23][24][25][26][27]. These three uropod-directed proteins have common features in leukocytes, localizing to the uropod in a manner that is dependent on core 1-derived O-glycans [28] and supporting the rolling of leukocytes, including T cells [29].…”